Background/Objectives: Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of patients with GBM. Using consensus clustering of MRI-based radiomic features, this study aims to investigate differential gene expression profiles based on radiomic clusters. Methods: Patients from the TCGA and CPTAC datasets (n = 114) were included in this study. Radiomic features including T1, T1 with contrast, T2, and FLAIR MRI sequences were extracted using PyRadiomics. Selected radiomic features were then clustered using ConsensusClusterPlus (k-means base algorithm and Euclidean distance), which iteratively subsamples and clusters 80% of the data to identify stable clusters by calculating the frequency in which each patient is a member of a cluster across iterations. Gene expression data (available for n = 69 patients) was analyzed using differential gene expression (DEG) and gene set enrichment (GSEA) approaches, after batch correction using ComBat-seq. Results: Three distinct clusters were identified based on the relative consensus matrix and cumulative distribution plots (Cluster 1, n = 25; Cluster 2, n = 46; Cluster 3, n = 43). No significant differences in patient demographic characteristics, MGMT methylation status, tumor location, or overall survival were identified across clusters. Differentially expressed genes were identified in Cluster 1, which have been previously associated with GBM prognosis, recurrence, and treatment sensitivity. GSEA of Cluster 1 showed an enrichment of genes upregulated for immune-related and DNA metabolism pathways and genes downregulated in pathways associated with protein and histone deacetylation. Clusters 2 and 3 exhibited fewer DEGs which failed to reach significance after multiple testing corrections. Conclusions: Consensus clustering of radiomic features revealed unique gene expression profiles in the GBM cohort which likely represent subtle differences in tumor biology and radiosensitivity that are not visually discernible, underscoring the potential of radiomics to serve as a non-invasive alternative for identifying GBM molecular heterogeneity. Further investigation is still required to validate these findings and their clinical implications.
背景/目的：胶质母细胞瘤（GBM）是最常见的恶性原发性中枢神经系统肿瘤，其预后和生存结局极差。影像组学特征提取等非侵入性方法通过评估亚视觉影像特征，为区分GBM患者群体的分子特征提供了潜在有力工具。本研究基于MRI影像组学特征的共识聚类，旨在探究不同影像组学聚类间的差异基因表达谱。方法：本研究纳入来自TCGA和CPTAC数据集的114例患者。使用PyRadiomics提取T1、T1增强、T2及FLAIR MRI序列的影像组学特征。通过ConsensusClusterPlus（基于k-means算法和欧氏距离）对筛选后的特征进行聚类分析，该方法通过迭代抽取80%数据进行聚类，根据患者在各次迭代中归属同一聚类的频率确定稳定聚类。对69例患者的基因表达数据采用ComBat-seq进行批次校正后，通过差异基因表达分析和基因集富集分析进行研究。结果：基于相对共识矩阵和累积分布图识别出三个独立聚类（聚类1：25例；聚类2：46例；聚类3：43例）。各聚类间在人口统计学特征、MGMT甲基化状态、肿瘤部位及总生存期方面均无显著差异。在聚类1中鉴定出的差异表达基因，既往研究已证实其与GBM预后、复发及治疗敏感性相关。聚类1的基因集富集分析显示，免疫相关通路和DNA代谢通路上调基因显著富集，而蛋白质和组蛋白去乙酰化相关通路基因则呈现下调。聚类2和3的差异表达基因较少，且经多重检验校正后未达显著性阈值。结论：影像组学特征的共识聚类揭示了GBM队列中独特的基因表达谱，这些特征可能反映了肿瘤生物学特性和放射敏感性的细微差异，而这些差异无法通过视觉识别，凸显了影像组学作为非侵入性手段识别GBM分子异质性的潜力。仍需进一步研究验证这些发现及其临床意义。
Radiomic Consensus Clustering in Glioblastoma and Association with Gene Expression Profiles
肿瘤(癌症)患者之家